Overview

Dataset statistics

Number of variables20
Number of observations10539
Missing cells0
Missing cells (%)0.0%
Duplicate rows23
Duplicate rows (%)0.2%
Total size in memory1.7 MiB
Average record size in memory168.0 B

Variable types

Numeric9
Categorical10
Text1

Alerts

parkingSpacePrice has constant value "0"Constant
Dataset has 23 (0.2%) duplicate rowsDuplicates
propertyType is highly imbalanced (70.0%)Imbalance
newDevelopment is highly imbalanced (71.0%)Imbalance
newDevelopmentFinished is highly imbalanced (96.2%)Imbalance
floor has 1886 (17.9%) zerosZeros
rooms has 382 (3.6%) zerosZeros

Reproduction

Analysis started2024-05-20 11:08:48.697274
Analysis finished2024-05-20 11:09:00.876291
Duration12.18 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

price
Real number (ℝ)

Distinct1657
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean362684.83
Minimum12000
Maximum799900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size164.7 KiB
2024-05-20T13:09:01.000940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile118100
Q1195000
median315000
Q3502000
95-th percentile749000
Maximum799900
Range787900
Interquartile range (IQR)307000

Descriptive statistics

Standard deviation200193.97
Coefficient of variation (CV)0.55197778
Kurtosis-0.7888905
Mean362684.83
Median Absolute Deviation (MAD)140000
Skewness0.59659045
Sum3.8223354 × 109
Variance4.0077624 × 1010
MonotonicityNot monotonic
2024-05-20T13:09:01.417487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000 109
 
1.0%
650000 97
 
0.9%
450000 96
 
0.9%
750000 91
 
0.9%
175000 87
 
0.8%
250000 87
 
0.8%
550000 84
 
0.8%
220000 75
 
0.7%
290000 75
 
0.7%
699000 75
 
0.7%
Other values (1647) 9663
91.7%
ValueCountFrequency (%)
12000 1
 
< 0.1%
26100 1
 
< 0.1%
48000 1
 
< 0.1%
50000 2
< 0.1%
50600 1
 
< 0.1%
50700 1
 
< 0.1%
51100 1
 
< 0.1%
51700 4
< 0.1%
52200 2
< 0.1%
52400 3
< 0.1%
ValueCountFrequency (%)
799900 1
 
< 0.1%
799500 1
 
< 0.1%
799000 56
0.5%
798000 5
 
< 0.1%
797500 1
 
< 0.1%
795000 50
0.5%
794500 1
 
< 0.1%
792000 3
 
< 0.1%
790000 44
0.4%
789000 56
0.5%

floor
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4193946
Minimum-2
Maximum60
Zeros1886
Zeros (%)17.9%
Negative80
Negative (%)0.8%
Memory size164.7 KiB
2024-05-20T13:09:01.565298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0
Q11
median2
Q34
95-th percentile6
Maximum60
Range62
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4821679
Coefficient of variation (CV)1.0259458
Kurtosis35.300279
Mean2.4193946
Median Absolute Deviation (MAD)1
Skewness3.2174514
Sum25498
Variance6.1611573
MonotonicityNot monotonic
2024-05-20T13:09:01.718706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 2690
25.5%
0 1886
17.9%
2 1698
16.1%
3 1534
14.6%
4 1124
10.7%
5 636
 
6.0%
6 373
 
3.5%
7 190
 
1.8%
8 103
 
1.0%
-1 79
 
0.7%
Other values (16) 226
 
2.1%
ValueCountFrequency (%)
-2 1
 
< 0.1%
-1 79
 
0.7%
0 1886
17.9%
1 2690
25.5%
2 1698
16.1%
3 1534
14.6%
4 1124
10.7%
5 636
 
6.0%
6 373
 
3.5%
7 190
 
1.8%
ValueCountFrequency (%)
60 1
 
< 0.1%
22 2
 
< 0.1%
21 3
 
< 0.1%
20 6
0.1%
19 2
 
< 0.1%
18 8
0.1%
17 13
0.1%
16 4
 
< 0.1%
15 12
0.1%
14 11
0.1%

propertyType
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
flat
9400 
penthouse
 
402
studio
 
351
duplex
 
284
chalet
 
102

Length

Max length9
Median length4
Mean length4.3305816
Min length4

Characters and Unicode

Total characters45640
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 9400
89.2%
penthouse 402
 
3.8%
studio 351
 
3.3%
duplex 284
 
2.7%
chalet 102
 
1.0%

Length

2024-05-20T13:09:01.877480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T13:09:02.025926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
flat 9400
89.2%
penthouse 402
 
3.8%
studio 351
 
3.3%
duplex 284
 
2.7%
chalet 102
 
1.0%

Most occurring characters

ValueCountFrequency (%)
t 10255
22.5%
l 9786
21.4%
a 9502
20.8%
f 9400
20.6%
e 1190
 
2.6%
u 1037
 
2.3%
o 753
 
1.6%
s 753
 
1.6%
p 686
 
1.5%
d 635
 
1.4%
Other values (5) 1643
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 10255
22.5%
l 9786
21.4%
a 9502
20.8%
f 9400
20.6%
e 1190
 
2.6%
u 1037
 
2.3%
o 753
 
1.6%
s 753
 
1.6%
p 686
 
1.5%
d 635
 
1.4%
Other values (5) 1643
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 10255
22.5%
l 9786
21.4%
a 9502
20.8%
f 9400
20.6%
e 1190
 
2.6%
u 1037
 
2.3%
o 753
 
1.6%
s 753
 
1.6%
p 686
 
1.5%
d 635
 
1.4%
Other values (5) 1643
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 10255
22.5%
l 9786
21.4%
a 9502
20.8%
f 9400
20.6%
e 1190
 
2.6%
u 1037
 
2.3%
o 753
 
1.6%
s 753
 
1.6%
p 686
 
1.5%
d 635
 
1.4%
Other values (5) 1643
 
3.6%

size
Real number (ℝ)

Distinct255
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.033077
Minimum11
Maximum619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size164.7 KiB
2024-05-20T13:09:02.185652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile38
Q160
median78
Q3103
95-th percentile150
Maximum619
Range608
Interquartile range (IQR)43

Descriptive statistics

Standard deviation37.921095
Coefficient of variation (CV)0.44595699
Kurtosis14.375056
Mean85.033077
Median Absolute Deviation (MAD)21
Skewness2.1877269
Sum896163.6
Variance1438.0094
MonotonicityNot monotonic
2024-05-20T13:09:02.398335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 252
 
2.4%
65 244
 
2.3%
70 240
 
2.3%
75 229
 
2.2%
80 221
 
2.1%
50 202
 
1.9%
100 169
 
1.6%
55 162
 
1.5%
90 160
 
1.5%
74 155
 
1.5%
Other values (245) 8505
80.7%
ValueCountFrequency (%)
11 1
 
< 0.1%
14 1
 
< 0.1%
15 4
 
< 0.1%
17 2
 
< 0.1%
18 2
 
< 0.1%
19 4
 
< 0.1%
20 7
 
0.1%
21 9
0.1%
22 20
0.2%
23 6
 
0.1%
ValueCountFrequency (%)
619 1
< 0.1%
558 1
< 0.1%
536 1
< 0.1%
480 1
< 0.1%
450 1
< 0.1%
429 1
< 0.1%
383 1
< 0.1%
361 1
< 0.1%
358 1
< 0.1%
354 1
< 0.1%

exterior
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
1
8164 
0
2375 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 8164
77.5%
0 2375
 
22.5%

Length

2024-05-20T13:09:02.600784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T13:09:02.733887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8164
77.5%
0 2375
 
22.5%

Most occurring characters

ValueCountFrequency (%)
1 8164
77.5%
0 2375
 
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 8164
77.5%
0 2375
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 8164
77.5%
0 2375
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 8164
77.5%
0 2375
 
22.5%

rooms
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3192903
Minimum0
Maximum9
Zeros382
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size164.7 KiB
2024-05-20T13:09:02.852720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0652729
Coefficient of variation (CV)0.45930985
Kurtosis1.2801159
Mean2.3192903
Median Absolute Deviation (MAD)1
Skewness0.33848285
Sum24443
Variance1.1348063
MonotonicityNot monotonic
2024-05-20T13:09:02.991713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 3782
35.9%
3 3406
32.3%
1 1834
17.4%
4 939
 
8.9%
0 382
 
3.6%
5 141
 
1.3%
6 34
 
0.3%
8 9
 
0.1%
7 9
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 382
 
3.6%
1 1834
17.4%
2 3782
35.9%
3 3406
32.3%
4 939
 
8.9%
5 141
 
1.3%
6 34
 
0.3%
7 9
 
0.1%
8 9
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
9 3
 
< 0.1%
8 9
 
0.1%
7 9
 
0.1%
6 34
 
0.3%
5 141
 
1.3%
4 939
 
8.9%
3 3406
32.3%
2 3782
35.9%
1 1834
17.4%
0 382
 
3.6%

bathrooms
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4535535
Minimum0
Maximum8
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size164.7 KiB
2024-05-20T13:09:03.142748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60050071
Coefficient of variation (CV)0.41312599
Kurtosis2.8918323
Mean1.4535535
Median Absolute Deviation (MAD)0
Skewness1.2518959
Sum15319
Variance0.3606011
MonotonicityNot monotonic
2024-05-20T13:09:03.289125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 6250
59.3%
2 3844
36.5%
3 389
 
3.7%
4 40
 
0.4%
5 8
 
0.1%
0 6
 
0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 6
 
0.1%
1 6250
59.3%
2 3844
36.5%
3 389
 
3.7%
4 40
 
0.4%
5 8
 
0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 1
 
< 0.1%
5 8
 
0.1%
4 40
 
0.4%
3 389
 
3.7%
2 3844
36.5%
1 6250
59.3%
0 6
 
0.1%

district
Categorical

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
Centro
1661 
Barrio de Salamanca
824 
Carabanchel
822 
Tetuán
735 
Ciudad Lineal
703 
Other values (16)
5794 

Length

Max length19
Median length17
Mean length9.8888889
Min length5

Characters and Unicode

Total characters104219
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVillaverde
2nd rowFuencarral
3rd rowArganzuela
4th rowPuente de Vallecas
5th rowBarrio de Salamanca

Common Values

ValueCountFrequency (%)
Centro 1661
15.8%
Barrio de Salamanca 824
 
7.8%
Carabanchel 822
 
7.8%
Tetuán 735
 
7.0%
Ciudad Lineal 703
 
6.7%
Puente de Vallecas 667
 
6.3%
Arganzuela 656
 
6.2%
Latina 460
 
4.4%
Chamberí 458
 
4.3%
Villaverde 437
 
4.1%
Other values (11) 3116
29.6%

Length

2024-05-20T13:09:03.476233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 1716
 
11.4%
centro 1661
 
11.0%
vallecas 892
 
5.9%
barrio 824
 
5.5%
salamanca 824
 
5.5%
carabanchel 822
 
5.4%
tetuán 735
 
4.9%
ciudad 703
 
4.7%
lineal 703
 
4.7%
puente 667
 
4.4%
Other values (16) 5558
36.8%

Most occurring characters

ValueCountFrequency (%)
a 17517
16.8%
e 11396
 
10.9%
r 8224
 
7.9%
n 7983
 
7.7%
l 7927
 
7.6%
t 4816
 
4.6%
4566
 
4.4%
o 4165
 
4.0%
C 3986
 
3.8%
i 3860
 
3.7%
Other values (25) 29779
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 104219
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 17517
16.8%
e 11396
 
10.9%
r 8224
 
7.9%
n 7983
 
7.7%
l 7927
 
7.6%
t 4816
 
4.6%
4566
 
4.4%
o 4165
 
4.0%
C 3986
 
3.8%
i 3860
 
3.7%
Other values (25) 29779
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 104219
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 17517
16.8%
e 11396
 
10.9%
r 8224
 
7.9%
n 7983
 
7.7%
l 7927
 
7.6%
t 4816
 
4.6%
4566
 
4.4%
o 4165
 
4.0%
C 3986
 
3.8%
i 3860
 
3.7%
Other values (25) 29779
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 104219
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 17517
16.8%
e 11396
 
10.9%
r 8224
 
7.9%
n 7983
 
7.7%
l 7927
 
7.6%
t 4816
 
4.6%
4566
 
4.4%
o 4165
 
4.0%
C 3986
 
3.8%
i 3860
 
3.7%
Other values (25) 29779
28.6%
Distinct131
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
2024-05-20T13:09:03.821548image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length31
Median length25
Mean length11.843154
Min length3

Characters and Unicode

Total characters124815
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSan Cristóbal
2nd rowPilar
3rd rowPalos de Moguer
4th rowPalomeras Bajas
5th rowLista
ValueCountFrequency (%)
san 541
 
3.4%
lavapiés-embajadores 495
 
3.1%
de 486
 
3.1%
del 438
 
2.8%
malasaña-universidad 433
 
2.7%
vista 267
 
1.7%
265
 
1.7%
goya 256
 
1.6%
puerta 245
 
1.5%
palacio 233
 
1.5%
Other values (162) 12170
76.9%
2024-05-20T13:09:04.383749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 18127
14.5%
e 10904
 
8.7%
s 8990
 
7.2%
r 7632
 
6.1%
i 7582
 
6.1%
l 7335
 
5.9%
o 6991
 
5.6%
5290
 
4.2%
n 5036
 
4.0%
d 4379
 
3.5%
Other values (47) 42549
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 124815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 18127
14.5%
e 10904
 
8.7%
s 8990
 
7.2%
r 7632
 
6.1%
i 7582
 
6.1%
l 7335
 
5.9%
o 6991
 
5.6%
5290
 
4.2%
n 5036
 
4.0%
d 4379
 
3.5%
Other values (47) 42549
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 124815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 18127
14.5%
e 10904
 
8.7%
s 8990
 
7.2%
r 7632
 
6.1%
i 7582
 
6.1%
l 7335
 
5.9%
o 6991
 
5.6%
5290
 
4.2%
n 5036
 
4.0%
d 4379
 
3.5%
Other values (47) 42549
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 124815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 18127
14.5%
e 10904
 
8.7%
s 8990
 
7.2%
r 7632
 
6.1%
i 7582
 
6.1%
l 7335
 
5.9%
o 6991
 
5.6%
5290
 
4.2%
n 5036
 
4.0%
d 4379
 
3.5%
Other values (47) 42549
34.1%

latitude
Real number (ℝ)

Distinct10009
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.418927
Minimum40.332238
Maximum40.506087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size164.7 KiB
2024-05-20T13:09:04.591907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum40.332238
5-th percentile40.367384
Q140.395786
median40.419328
Q340.438077
95-th percentile40.476798
Maximum40.506087
Range0.1738495
Interquartile range (IQR)0.04229105

Descriptive statistics

Standard deviation0.033146053
Coefficient of variation (CV)0.00082006268
Kurtosis-0.24249605
Mean40.418927
Median Absolute Deviation (MAD)0.0212791
Skewness0.067513552
Sum425975.07
Variance0.0010986608
MonotonicityNot monotonic
2024-05-20T13:09:04.772424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.4679837 22
 
0.2%
40.4858604 15
 
0.1%
40.3677689 11
 
0.1%
40.4507677 11
 
0.1%
40.4627395 11
 
0.1%
40.41962 10
 
0.1%
40.4342056 10
 
0.1%
40.3739308 9
 
0.1%
40.3414344 9
 
0.1%
40.4614362 9
 
0.1%
Other values (9999) 10422
98.9%
ValueCountFrequency (%)
40.3322377 1
< 0.1%
40.3325827 1
< 0.1%
40.3327774 1
< 0.1%
40.333354 1
< 0.1%
40.3333909 1
< 0.1%
40.3333954 2
< 0.1%
40.3334364 1
< 0.1%
40.3339494 1
< 0.1%
40.3341865 1
< 0.1%
40.3343306 1
< 0.1%
ValueCountFrequency (%)
40.5060872 1
< 0.1%
40.5057791 1
< 0.1%
40.505589 1
< 0.1%
40.5046934 1
< 0.1%
40.5045779 1
< 0.1%
40.5045573 1
< 0.1%
40.503504 1
< 0.1%
40.5026781 1
< 0.1%
40.5025537 1
< 0.1%
40.5025061 1
< 0.1%

longitude
Real number (ℝ)

Distinct9984
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.6885828
Minimum-3.8037169
Maximum-3.5899988
Zeros0
Zeros (%)0.0%
Negative10539
Negative (%)100.0%
Memory size164.7 KiB
2024-05-20T13:09:04.967561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-3.8037169
5-th percentile-3.7458699
Q1-3.7086616
median-3.6967106
Q3-3.6671438
95-th percentile-3.6219436
Maximum-3.5899988
Range0.2137181
Interquartile range (IQR)0.0415178

Descriptive statistics

Standard deviation0.035468867
Coefficient of variation (CV)-0.0096158519
Kurtosis0.077315897
Mean-3.6885828
Median Absolute Deviation (MAD)0.0213974
Skewness0.19228403
Sum-38873.975
Variance0.0012580405
MonotonicityNot monotonic
2024-05-20T13:09:05.145008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.7086485 22
 
0.2%
-3.6686277 15
 
0.1%
-3.7079717 11
 
0.1%
-3.7690282 11
 
0.1%
-3.7591247 11
 
0.1%
-3.62887 10
 
0.1%
-3.6246509 10
 
0.1%
-3.7034353 9
 
0.1%
-3.6773425 9
 
0.1%
-3.6975181 9
 
0.1%
Other values (9974) 10422
98.9%
ValueCountFrequency (%)
-3.8037169 1
< 0.1%
-3.8016081 1
< 0.1%
-3.8011524 1
< 0.1%
-3.8004199 1
< 0.1%
-3.7993248 1
< 0.1%
-3.7970909 1
< 0.1%
-3.7965106 1
< 0.1%
-3.7961704 1
< 0.1%
-3.795635 1
< 0.1%
-3.7929282 1
< 0.1%
ValueCountFrequency (%)
-3.5899988 1
< 0.1%
-3.591129 1
< 0.1%
-3.5919293 1
< 0.1%
-3.5919686 1
< 0.1%
-3.5925219 1
< 0.1%
-3.5928453 1
< 0.1%
-3.5934814 1
< 0.1%
-3.5938181 2
< 0.1%
-3.594511 1
< 0.1%
-3.5948268 1
< 0.1%

distance
Real number (ℝ)

Distinct5944
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4252.4464
Minimum13
Maximum9991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size164.7 KiB
2024-05-20T13:09:05.316454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile650
Q12281.5
median4062
Q36085.5
95-th percentile8397
Maximum9991
Range9978
Interquartile range (IQR)3804

Descriptive statistics

Standard deviation2472.2708
Coefficient of variation (CV)0.58137612
Kurtosis-0.94556783
Mean4252.4464
Median Absolute Deviation (MAD)1873
Skewness0.23874732
Sum44816533
Variance6112123
MonotonicityNot monotonic
2024-05-20T13:09:05.501333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5720 23
 
0.2%
8228 18
 
0.2%
7210 12
 
0.1%
482 11
 
0.1%
7562 11
 
0.1%
6304 11
 
0.1%
3808 11
 
0.1%
4997 10
 
0.1%
6931 10
 
0.1%
6973 10
 
0.1%
Other values (5934) 10412
98.8%
ValueCountFrequency (%)
13 1
< 0.1%
20 1
< 0.1%
37 1
< 0.1%
40 1
< 0.1%
42 1
< 0.1%
58 1
< 0.1%
60 1
< 0.1%
62 1
< 0.1%
66 1
< 0.1%
67 1
< 0.1%
ValueCountFrequency (%)
9991 1
< 0.1%
9976 1
< 0.1%
9965 1
< 0.1%
9960 1
< 0.1%
9959 1
< 0.1%
9952 1
< 0.1%
9949 1
< 0.1%
9947 1
< 0.1%
9945 1
< 0.1%
9944 1
< 0.1%

status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
good
8196 
renew
1808 
newdevelopment
 
535

Length

Max length14
Median length4
Mean length4.6791916
Min length4

Characters and Unicode

Total characters49314
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrenew
2nd rowgood
3rd rowgood
4th rowgood
5th rowgood

Common Values

ValueCountFrequency (%)
good 8196
77.8%
renew 1808
 
17.2%
newdevelopment 535
 
5.1%

Length

2024-05-20T13:09:05.668792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T13:09:05.807472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
good 8196
77.8%
renew 1808
 
17.2%
newdevelopment 535
 
5.1%

Most occurring characters

ValueCountFrequency (%)
o 16927
34.3%
d 8731
17.7%
g 8196
16.6%
e 5756
 
11.7%
n 2878
 
5.8%
w 2343
 
4.8%
r 1808
 
3.7%
v 535
 
1.1%
l 535
 
1.1%
p 535
 
1.1%
Other values (2) 1070
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49314
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 16927
34.3%
d 8731
17.7%
g 8196
16.6%
e 5756
 
11.7%
n 2878
 
5.8%
w 2343
 
4.8%
r 1808
 
3.7%
v 535
 
1.1%
l 535
 
1.1%
p 535
 
1.1%
Other values (2) 1070
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49314
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 16927
34.3%
d 8731
17.7%
g 8196
16.6%
e 5756
 
11.7%
n 2878
 
5.8%
w 2343
 
4.8%
r 1808
 
3.7%
v 535
 
1.1%
l 535
 
1.1%
p 535
 
1.1%
Other values (2) 1070
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49314
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 16927
34.3%
d 8731
17.7%
g 8196
16.6%
e 5756
 
11.7%
n 2878
 
5.8%
w 2343
 
4.8%
r 1808
 
3.7%
v 535
 
1.1%
l 535
 
1.1%
p 535
 
1.1%
Other values (2) 1070
 
2.2%

newDevelopment
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
0
10004 
1
 
535

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10004
94.9%
1 535
 
5.1%

Length

2024-05-20T13:09:05.940674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T13:09:06.050611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 10004
94.9%
1 535
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 10004
94.9%
1 535
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10004
94.9%
1 535
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10004
94.9%
1 535
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10004
94.9%
1 535
 
5.1%

hasLift
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
1
7107 
0
3432 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7107
67.4%
0 3432
32.6%

Length

2024-05-20T13:09:06.188545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T13:09:06.319901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 7107
67.4%
0 3432
32.6%

Most occurring characters

ValueCountFrequency (%)
1 7107
67.4%
0 3432
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7107
67.4%
0 3432
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7107
67.4%
0 3432
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7107
67.4%
0 3432
32.6%

priceByArea
Real number (ℝ)

Distinct4422
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4420.8243
Minimum197
Maximum16304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size164.7 KiB
2024-05-20T13:09:06.472206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum197
5-th percentile1764.8
Q12800
median4067
Q35544
95-th percentile8537
Maximum16304
Range16107
Interquartile range (IQR)2744

Descriptive statistics

Standard deviation2114.6684
Coefficient of variation (CV)0.47834256
Kurtosis0.98499972
Mean4420.8243
Median Absolute Deviation (MAD)1353
Skewness0.98237171
Sum46591067
Variance4471822.4
MonotonicityNot monotonic
2024-05-20T13:09:06.666960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 82
 
0.8%
2500 37
 
0.4%
4000 36
 
0.3%
3000 32
 
0.3%
8500 31
 
0.3%
3750 31
 
0.3%
4500 30
 
0.3%
3500 30
 
0.3%
3333 26
 
0.2%
5500 25
 
0.2%
Other values (4412) 10179
96.6%
ValueCountFrequency (%)
197 1
< 0.1%
442 1
< 0.1%
596 1
< 0.1%
619 1
< 0.1%
626 1
< 0.1%
698 1
< 0.1%
771 1
< 0.1%
814 1
< 0.1%
836 1
< 0.1%
837 1
< 0.1%
ValueCountFrequency (%)
16304 1
< 0.1%
14706 1
< 0.1%
14444 1
< 0.1%
13889 1
< 0.1%
13800 1
< 0.1%
13676 1
< 0.1%
13300 1
< 0.1%
13105 1
< 0.1%
12931 1
< 0.1%
12903 1
< 0.1%

newDevelopmentFinished
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
0
10497 
1
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10497
99.6%
1 42
 
0.4%

Length

2024-05-20T13:09:06.845622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T13:09:06.957504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 10497
99.6%
1 42
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 10497
99.6%
1 42
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10497
99.6%
1 42
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10497
99.6%
1 42
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10497
99.6%
1 42
 
0.4%

hasParkingSpace
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
0
8410 
1
2129 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8410
79.8%
1 2129
 
20.2%

Length

2024-05-20T13:09:07.074842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T13:09:07.196400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8410
79.8%
1 2129
 
20.2%

Most occurring characters

ValueCountFrequency (%)
0 8410
79.8%
1 2129
 
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8410
79.8%
1 2129
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8410
79.8%
1 2129
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8410
79.8%
1 2129
 
20.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
0
8653 
1
1886 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10539
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8653
82.1%
1 1886
 
17.9%

Length

2024-05-20T13:09:07.324641image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T13:09:07.446911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8653
82.1%
1 1886
 
17.9%

Most occurring characters

ValueCountFrequency (%)
0 8653
82.1%
1 1886
 
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8653
82.1%
1 1886
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8653
82.1%
1 1886
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8653
82.1%
1 1886
 
17.9%

parkingSpacePrice
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size164.7 KiB
0
10539 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10539
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10539
100.0%

Length

2024-05-20T13:09:07.580461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T13:09:07.701612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 10539
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10539
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10539
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10539
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10539
100.0%

Interactions

2024-05-20T13:08:59.047866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:49.323462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:50.557913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:51.678421image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:52.880447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:54.005287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:55.474986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:56.728673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:57.899057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:59.185308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:49.479157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:50.689514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:51.813278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:53.008371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:54.145284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:55.628450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:56.875264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:58.040979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:59.332545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:49.609889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:50.810603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:51.942662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:53.131433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:54.287986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:55.759427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:57.004414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:58.164658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:59.481580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:49.741060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:50.938048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:52.065617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:53.260864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:54.420908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:55.902660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:57.124856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:58.285266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:59.615171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:49.869128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:51.043196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:52.189800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:53.392401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:54.553085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:56.023666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:57.230354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:58.402750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:59.755958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:50.012145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:51.181515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:52.320270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:53.526230image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:54.917877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:56.172588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:57.355277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:58.551060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:59.889176image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:50.159662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:51.307055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:52.453365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:53.647682image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:55.058977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:56.317653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:57.495894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:58.699480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:09:00.013907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:50.288425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:51.431548image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:52.587238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:53.764941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:55.185771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:56.458503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:57.622144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:58.809480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:09:00.137567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:50.417891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:51.549063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:52.729572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:53.880131image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:55.315554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:56.582781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:57.759893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-20T13:08:58.916325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-20T13:09:00.354465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-20T13:09:00.708893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

pricefloorpropertyTypesizeexteriorroomsbathroomsdistrictneighborhoodlatitudelongitudedistancestatusnewDevelopmenthasLiftpriceByAreanewDevelopmentFinishedhasParkingSpaceisParkingSpaceIncludedInPriceparkingSpacePrice
191000.00flat85.0132VillaverdeSan Cristóbal40.343316-3.6922178212renew001071.00000
2279000.03flat129.0122FuencarralPilar40.473896-3.7081386373good012163.00000
3350000.01flat93.0131ArganzuelaPalos de Moguer40.401887-3.6942551814good013763.00000
470700.04flat113.0132Puente de VallecasPalomeras Bajas40.381567-3.6549235657good01626.00000
5249000.0-1flat57.0011Barrio de SalamancaLista40.430780-3.6740462926good014368.00000
6195000.01chalet70.0021San BlasSimancas40.429233-3.6266996627good002786.00000
777400.00flat53.0011VicálvaroCasco Histórico de Vicálvaro40.401111-3.6025488700good001460.00000
8420000.03flat80.0121ArganzuelaAcacias40.402647-3.7139061804good015250.00000
9120000.04flat71.0021CarabanchelVista Alegre40.388903-3.7346044074good001690.00000
10510000.00flat71.0022Barrio de SalamancaGoya40.424144-3.6784742254good017183.00000
pricefloorpropertyTypesizeexteriorroomsbathroomsdistrictneighborhoodlatitudelongitudedistancestatusnewDevelopmenthasLiftpriceByAreanewDevelopmentFinishedhasParkingSpaceisParkingSpaceIncludedInPriceparkingSpacePrice
14540719000.02flat73.0022Barrio de SalamancaGoya40.423817-3.6762642417good019849.00000
14541795000.05penthouse155.0142CentroLavapiés-Embajadores40.412125-3.707479622good015129.00000
14542650000.00flat75.0111CentroHuertas-Cortes40.412238-3.696491757good008667.00000
14543299000.02flat122.0132Ciudad LinealPueblo Nuevo40.434271-3.6397915715good002451.00110
14545435000.00flat80.0131CentroMalasaña-Universidad40.423430-3.706677802good015438.00000
14546699000.01flat145.0132HortalezaSanchinarro40.496618-3.6518889891good014821.00110
14547299000.00flat105.0131TetuánVentilla-Almenara40.466234-3.6930565574good012848.00000
14548359900.02flat70.0122TetuánValdeacederas40.467390-3.7023845636newdevelopment115141.00110
14549306000.02flat95.0122VicálvaroAmbroz40.407101-3.6042538448newdevelopment113221.00000
14550394900.00flat153.0122TetuánValdeacederas40.467390-3.7023845636newdevelopment112581.00110

Duplicate rows

Most frequently occurring

pricefloorpropertyTypesizeexteriorroomsbathroomsdistrictneighborhoodlatitudelongitudedistancestatusnewDevelopmenthasLiftpriceByAreanewDevelopmentFinishedhasParkingSpaceisParkingSpaceIncludedInPriceparkingSpacePrice# duplicates
19679000.03flat65.0022Barrio de SalamancaLista40.429444-3.6716923023good0110446.000004
21719000.02flat73.0022Barrio de SalamancaGoya40.423133-3.6776752280good019849.000004
13520000.04flat104.0122ArganzuelaImperial40.405127-3.7142821590good015000.001003
0145000.0-1studio35.0101CentroLavapiés-Embajadores40.413292-3.702048392good014143.000002
1160000.01flat54.0011LatinaPuerta del Ángel40.406616-3.7337832817good012963.000002
2250000.04flat40.0011CentroMalasaña-Universidad40.425737-3.7093001127renew016250.000002
3255900.05flat80.0131Ciudad LinealPueblo Nuevo40.423878-3.6354205797good013199.000002
4274000.01flat113.0132VillaverdeButarque40.341434-3.6773428651good012425.001102
5319000.00flat50.0111ChamberíArapiles40.436498-3.7111702301good016380.000002
6329900.03flat80.0122Ciudad LinealColina40.457567-3.6604595810good004124.000002